AbstractThis paper discusses ensembles of simple but heterogeneous classiﬁers for word-sense disambiguation, examining the Stanford-CS224N system entered in the SENSEVAL-2 English lexical sample task. First-order classiﬁers are combined by a second-order classiﬁer, which variously uses majority voting, weighted voting, or a maximum entropy model. While individual ﬁrst-order classiﬁers perform comparably to middle-scoring teams’ systems, the combination achieves high performance. We discuss trade-offs and empirical performance. Finally, we present an analysis of the combination, examining how ensemble performance depends on error independence and task difﬁculty.
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